Hi, I'm Nidhi Menon

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From prototype to production, I build ML systems that scale — and lead the teams that ship them.

Open to ML Engineer roles in San Francisco Bay Area

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About Me

Nidhi Menon

I'm a Machine Learning Engineer with 6+ years of experience building and shipping production ML systems at scale. At Autodesk, I led cross-functional teams delivering AI-powered experiences to millions of users. My work includes a personalized recommendation system serving 708K daily active users and a GenAI and NLP-powered customer support insights platform that reduced support ticket volume by 10.7%.

I thrive at the intersection of technical execution, strategic thinking, and people leadership — owning roadmap and architecture decisions while partnering closely with product, design, and infrastructure leadership. I've architected end-to-end ML systems using Wide & Deep Networks, BERT, and RAG pipelines, while mentoring engineers and championing privacy-first AI development. I hold an M.S. in Computer Science (Machine Learning) from Georgia Tech and completed Stanford's NLP with Deep Learning program.

Outside of work, I'm an avid reader (and lifelong Harry Potter fan), a trained Bharatanatyam artist who performs at venues across the San Francisco Bay Area, and a creative tinkerer who loves turning everyday materials into something new. I'm currently pursuing a dance degree in Kathak. Giving back is a core part of who I am — I've been a member of Asha for Education for nearly a decade, volunteering and fundraising for causes centered around education and the upliftment of women, children, and underprivileged communities. At Autodesk, I led the Girls Who Code program for two years and helped organize the TechWomen initiative.

After a career break starting April 2025 to welcome my daughter — the ultimate crash course in patience and data-driven problem-solving — I'm energized and exploring my next ML engineering role.

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Experience

Work Experience

Autodesk

San Francisco, CA · 6 yrs 4 mos

Principal Machine Learning Engineer

May 2022 – Apr 2025

  • Led 12-person cross-functional team through 0-to-1 architecture, development, and production launch of a personalized recommendation system for Autodesk App Store, driving 13% lift in app downloads across ~708K DAU with <100ms latency
    • Owned end-to-end system architecture for real-time ML inference at scale using Wide & Deep Networks with online feature serving, achieving <50ms p95 inference latency
    • Built feature store and data migration pipeline, reducing model training time by 40% and infrastructure costs by 25%
    • Mentored an intern in building functional prototype, guiding algorithm selection and system design decisions
  • Led 0-to-1 design and implementation of ML-powered customer support insights platform, proactively surfacing product issues and enabling natural-language querying across 10,000+ quarterly tickets
    • Piloted BERT-based topic modeling framework in partnership with Autodesk Platform Services, surfacing 7 critical product issues leading to targeted fixes that reduced quarterly support volume by 10.7%
    • Evolved system from topic-based categorization to a RAG architecture, leveraging vector embeddings, FAISS, and cross-encoder re-ranking to surface contextually relevant insights beyond predefined topics
    • Designed a modular, extensible platform with configurable data connectors, enabling 3 additional product teams to adopt automated ticket analysis with minimal engineering overhead
  • Served as organizational Privacy Champion, conducting privacy impact assessments across ML initiatives, enforcing data retention and ethics standards, training teams on privacy best practices, and partnering with legal on open-source compliance

Senior Machine Learning Engineer

Jan 2019 – Apr 2022

  • Engineered high-performance data compression framework reducing API logs (~1.4GB/hr) by 99.8%, enabling scalable pipelines for:
    • Anomaly detection to reduce API usage abuse and obtain granular customer behavior insights
    • Driving strategic decisions on API monetization, product subscriptions, and customer retention
  • Contributed machine learning expertise to two pro bono projects for Autodesk Foundation, supporting initiatives for Hope Street Group and Yuno Technologies with predictive modeling and analytics
  • Championed Data-as-a-Service (DaaS) adoption across engineering teams, increasing Blameless Postmortem completion from 20% to 75%, fostering incident learning with self-service analytics

Data Science Intern

May – Aug 2018

  • Developed XGBoost models to forecast team velocity and sprint burn rates, enabling proactive agile coaching interventions, resulting in a 12.3% increase in organizational burn rate
  • Built an interactive visualization tool to analyze GitHub data, integrating APIs for real-time tracking of employee contributions to Autodesk's open-source projects, fostering developer community engagement

Georgia Institute of Technology

Atlanta, GA

Graduate Teaching Assistant

Aug – Dec 2018

  • TA for CS 4476: Introduction to Computer Vision (165 students) under Dr. Devi Parikh
  • Coordinated with the teaching team, graded assignments and projects in MATLAB/Python, and held office hours

SmartTurtles Inc.

Mumbai, India

Software Intern

Aug 2015 – Feb 2017

  • Built web applications for clients using agile methodology, focusing on client-side programming
  • Performed manual testing to ensure applications met customer needs before deployment

LTI (L&T Infotech)

Chennai, India

Project Trainee

Jun – Jul 2015

  • Designed a secure browser-based portal (Epic Essentials) for the Strasz Exam Management Suite, enabling certification and testing organizations to deliver better tests more efficiently

Research Experience

Georgia Institute of Technology

Atlanta, GA · Jan – Dec 2018

Graduate Student Researcher

Advisor: Dr. Dobromir Rahnev

  • Optimized and performed comparative study of deep CNN architectures (VGG-16/19, ResNet50, InceptionResNetV2) for hybrid image classification, achieving 94% accuracy through systematic hyperparameter tuning
  • Conducted comparative analysis of model predictions to draw parallels between neural network features and human cognitive processes, contributing to interdisciplinary research in AI and neuroscience

University of Mumbai

Mumbai, India · Jul 2016 – May 2017

Undergraduate Researcher

Advisor: Prof. Varunakshi Bhojane

  • Publication: Floor Layout Planning using Artificial Intelligence Technique · IJIRSET, Vol. 6, Issue 4, April 2017
  • Prototyped Genetic Algorithm software for furniture placement optimization, achieving 90% accuracy in space utilization by processing 20,000 positional chromosome configurations across 5 generations

Education

Stanford University

AI Professional Program — NLP with Deep Learning · May – Aug 2022

Georgia Institute of Technology

M.S. in Computer Science (Machine Learning) · Aug 2017 – Dec 2018

University of Mumbai (Pillai College of Engineering)

B.E. in Computer Engineering · Aug 2013 – May 2017

Skills

Languages

Python Java C MATLAB R

Frameworks

FastAPI Flask PyTorch TensorFlow

AI / ML

RAG FAISS BERT Wide & Deep Networks Ollama Hugging Face

Data

SQL DBT Snowflake DynamoDB Redshift Hadoop Hive

Tools

AWS Airflow Git Docker PowerBI Looker

Personal Projects

In Progress

HealthSteward

Privacy-first AI health coordination system

Consolidates fragmented medical records across multiple providers and chronic conditions. No health data is transmitted to external services unless explicitly anonymized — all sensitive processing stays local.

  • AI-powered visit prep that generates personalized questions for upcoming appointments
  • Local PDF parsing of after-visit summaries via Ollama with qwen2.5:7b — no cloud required
  • PII anonymization layer for any external LLM calls, combining deterministic parsing with targeted LLM calls for unstructured sections
FastAPI SQLAlchemy SQLite React 19 TypeScript Tailwind CSS Claude API Ollama
View on GitHub

Get In Touch

After a rewarding career break to welcome my daughter, I'm energized and ready to drive impact with purpose-driven teams. I'm currently exploring Machine Learning Engineer opportunities — let's connect.

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